Presented at http://mcbios-maqc.org. The FAIR Principles have propelled the global debate in all disciplines about better RDM, transparent and reproducible data worldwide, and in all disciplines. FAIR has de facto become a global norm for good RDM, a prerequisite for data science, since their endorsement by global and intergovernmental leaders. Funding bodies are consolidating FAIR into their funding agreements; publishers have united behind FAIR as a way to remain at the forefront of open research; and in the private sector FAIR is adopted and enshrined in policy in major biopharmas, libraries, and unions. FAIR is changing the culture of data science, but work is needed to turn the principles into reality. I will use the work of the FAIRplus project as examplar to illustrate challenges and progresses.
Presentation to the EOSC workshop on policies (https://www.google.com/url?q=https://eoscfuture.eu/eventsfuture/monitoring-eosc-readiness-fair-data-policies) on what FAIRsharing does for policies, including providing registration, discovery, flexible and clearer descriptions, relationships, machine readability and comparability.
Presentation to the EC Workshop on Maximizing investments in health research: FAIR data for a coordinate COVID-19 response. Workshop I, October 11, 2021.
The role of FAIRsharing in assessing FAIRness of digital objects: we assist, not assess. The workshop brought together a number of FAIR evaluation tools to discuss and design common FAIR tests to ensure tools deliver consistet results. Our presentation illustrates how FAIRsharing's content helps and how FAIRsharing's service contributes. The work will contribute to the work of the EOSC FAIR Metrics Task Force.
FAIRsharing: curating an ecosystem of research standards and databasesAllyson Lister
FAIRsharing is an informative and educational resource on interlinked standards, databases and policies, three key elements of the FAIR ecosystem. FAIRsharing is adopted by funders, publishers and communities across all research disciplines. It promotes the existence and value of these resources to aid data sharing and consequently requires a high standard of curation to ensure accurate and timely information is provided for all of our stakeholder groups. Here we discuss the methods employed and challenges faced during curation and maintenance of existing content as well as the introduction of new features. We will describe how our curation team uses a blend of manual and semi-automated curation to work on individual records and across large subsets of the registry. We also will discuss the benefits of both in-house curation and community-driven curation provided by our stakeholder groups.
Presentation to the "FAIRification put into practice: Characterization of energy data and development of workflows" event by https://www.eeradata.eu => https://www.eeradata.eu/event/2857:online-discussion-fairification-put-into-practice-characterization-of-energy-data-and-development-of-workflows.html#
Presented at http://mcbios-maqc.org. The FAIR Principles have propelled the global debate in all disciplines about better RDM, transparent and reproducible data worldwide, and in all disciplines. FAIR has de facto become a global norm for good RDM, a prerequisite for data science, since their endorsement by global and intergovernmental leaders. Funding bodies are consolidating FAIR into their funding agreements; publishers have united behind FAIR as a way to remain at the forefront of open research; and in the private sector FAIR is adopted and enshrined in policy in major biopharmas, libraries, and unions. FAIR is changing the culture of data science, but work is needed to turn the principles into reality. I will use the work of the FAIRplus project as examplar to illustrate challenges and progresses.
Presentation to the EOSC workshop on policies (https://www.google.com/url?q=https://eoscfuture.eu/eventsfuture/monitoring-eosc-readiness-fair-data-policies) on what FAIRsharing does for policies, including providing registration, discovery, flexible and clearer descriptions, relationships, machine readability and comparability.
Presentation to the EC Workshop on Maximizing investments in health research: FAIR data for a coordinate COVID-19 response. Workshop I, October 11, 2021.
The role of FAIRsharing in assessing FAIRness of digital objects: we assist, not assess. The workshop brought together a number of FAIR evaluation tools to discuss and design common FAIR tests to ensure tools deliver consistet results. Our presentation illustrates how FAIRsharing's content helps and how FAIRsharing's service contributes. The work will contribute to the work of the EOSC FAIR Metrics Task Force.
FAIRsharing: curating an ecosystem of research standards and databasesAllyson Lister
FAIRsharing is an informative and educational resource on interlinked standards, databases and policies, three key elements of the FAIR ecosystem. FAIRsharing is adopted by funders, publishers and communities across all research disciplines. It promotes the existence and value of these resources to aid data sharing and consequently requires a high standard of curation to ensure accurate and timely information is provided for all of our stakeholder groups. Here we discuss the methods employed and challenges faced during curation and maintenance of existing content as well as the introduction of new features. We will describe how our curation team uses a blend of manual and semi-automated curation to work on individual records and across large subsets of the registry. We also will discuss the benefits of both in-house curation and community-driven curation provided by our stakeholder groups.
Presentation to the "FAIRification put into practice: Characterization of energy data and development of workflows" event by https://www.eeradata.eu => https://www.eeradata.eu/event/2857:online-discussion-fairification-put-into-practice-characterization-of-energy-data-and-development-of-workflows.html#
Westminster Higher Education Forum policy conference Open research data in the UK: https://www.westminsterforumprojects.co.uk/conference/open-research-data-20
The FAIR Cookbook poster, as presented at the ELIXIR-UK Node and the UK Conference of Bioinformatics and Computational Biology 2021: https://www.earlham.ac.uk/uk-conference-bioinformatics-and-computational-biology-21
The FAIR Cookbook poster, as presented at the UK Conference of Bioinformatics and Computational Biology 2021: https://www.earlham.ac.uk/uk-conference-bioinformatics-and-computational-biology-21
Breif overview of FAIR and FAIRsharing, with focus on publishers for the Euroscience Open Forum (ESOF) 2020 session on FAIR and Data Sharing:
https://www.esof.eu/en/programme/programme-event-list-all-events/event-information/scientific-data-sharing-and-its-impact-on-scientific-careers-and-their-evaluation.html
Brief introduction to FAIRsharing work with industry (publishers, pharmas) and the FAIR Cookbook (for the Life Science): https://www.opensciencefair.eu/2021/workshops/applying-fair-principles-to-open-science-and-industry-to-drive-innovation-challenges-and-opportunities
Breif overview of the FAIR Cookbook for the UK Conference of Bioinformatics and Computational Biology 2021: https://www.earlham.ac.uk/uk-conference-bioinformatics-and-computational-biology-21
Presentation to the EC Workshop on Maximizing investments in health research: FAIR data for a coordinate COVID-19 response. Workshop III, November 8, 2021.
Brief summary for the INCF Neuroscience Assembly (https://neuroinformatics.incf.org/2021/program-week-2) of the two sessions run at the RDA Plenary 17th, which FAIRsharing WG has contributed t.
"Standards landscape" NIF Big Data 2 Knowledge (BD2K) Initiative, Sep, 2013Susanna-Assunta Sansone
Overview of the landscape of standards in life sciences for the NIH BD2K
"Frameworks for Community-Based Standards Efforts" workshop
September 25, 2013 - September 26, 2013
Co-Chairs: Susanna Sansone, PhD and David Kennedy PhD.
The overall goal of this workshop is to learn what has worked and what has not worked in community-based standards efforts. Participants will have experience in leading specific community based standards initiatives. Prior to the workshop, participants will be asked to address in writing answers to specific questions regarding formulating, conducting, and maintaining such efforts. This information will be used to facilitate focused and actionable discussion at the workshop. Issuance of a Request for Information soliciting comment from the broader community on some of the key issues addressed in the workshop is currently envisioned.
Contact: BD2Kworkshops@mail.nih.gov
Agenda: Frameworks for Community-Based Standards Efforts (PDF 40.7KB)
Participant List: Roster of Invited Participants (PDF 32KB)
Forum (Join the discussion): http://frameworks.prophpbb.com
Watch Live: http://videocast.nih.gov/summary.asp?live=13088 - See more at: http://bd2k.nih.gov/workshops.html#cbse
Overview of metadata standards, and how FAIRsharing and the FAIR Cookbook help selecting and using them. Presentation to the What is metadata? Common standards and properties. EHP Workshop, November 9, 2022: https://ephconference.eu/pre-conference-programme-441
Westminster Higher Education Forum policy conference Open research data in the UK: https://www.westminsterforumprojects.co.uk/conference/open-research-data-20
The FAIR Cookbook poster, as presented at the ELIXIR-UK Node and the UK Conference of Bioinformatics and Computational Biology 2021: https://www.earlham.ac.uk/uk-conference-bioinformatics-and-computational-biology-21
The FAIR Cookbook poster, as presented at the UK Conference of Bioinformatics and Computational Biology 2021: https://www.earlham.ac.uk/uk-conference-bioinformatics-and-computational-biology-21
Breif overview of FAIR and FAIRsharing, with focus on publishers for the Euroscience Open Forum (ESOF) 2020 session on FAIR and Data Sharing:
https://www.esof.eu/en/programme/programme-event-list-all-events/event-information/scientific-data-sharing-and-its-impact-on-scientific-careers-and-their-evaluation.html
Brief introduction to FAIRsharing work with industry (publishers, pharmas) and the FAIR Cookbook (for the Life Science): https://www.opensciencefair.eu/2021/workshops/applying-fair-principles-to-open-science-and-industry-to-drive-innovation-challenges-and-opportunities
Breif overview of the FAIR Cookbook for the UK Conference of Bioinformatics and Computational Biology 2021: https://www.earlham.ac.uk/uk-conference-bioinformatics-and-computational-biology-21
Presentation to the EC Workshop on Maximizing investments in health research: FAIR data for a coordinate COVID-19 response. Workshop III, November 8, 2021.
Brief summary for the INCF Neuroscience Assembly (https://neuroinformatics.incf.org/2021/program-week-2) of the two sessions run at the RDA Plenary 17th, which FAIRsharing WG has contributed t.
"Standards landscape" NIF Big Data 2 Knowledge (BD2K) Initiative, Sep, 2013Susanna-Assunta Sansone
Overview of the landscape of standards in life sciences for the NIH BD2K
"Frameworks for Community-Based Standards Efforts" workshop
September 25, 2013 - September 26, 2013
Co-Chairs: Susanna Sansone, PhD and David Kennedy PhD.
The overall goal of this workshop is to learn what has worked and what has not worked in community-based standards efforts. Participants will have experience in leading specific community based standards initiatives. Prior to the workshop, participants will be asked to address in writing answers to specific questions regarding formulating, conducting, and maintaining such efforts. This information will be used to facilitate focused and actionable discussion at the workshop. Issuance of a Request for Information soliciting comment from the broader community on some of the key issues addressed in the workshop is currently envisioned.
Contact: BD2Kworkshops@mail.nih.gov
Agenda: Frameworks for Community-Based Standards Efforts (PDF 40.7KB)
Participant List: Roster of Invited Participants (PDF 32KB)
Forum (Join the discussion): http://frameworks.prophpbb.com
Watch Live: http://videocast.nih.gov/summary.asp?live=13088 - See more at: http://bd2k.nih.gov/workshops.html#cbse
Overview of metadata standards, and how FAIRsharing and the FAIR Cookbook help selecting and using them. Presentation to the What is metadata? Common standards and properties. EHP Workshop, November 9, 2022: https://ephconference.eu/pre-conference-programme-441
Overview of FAIR and the IMI FAIRplus project at the UK Conference of Bioinformatics and Computational Biology 2020: https://www.earlham.ac.uk/uk-conference-bioinformatics-and-computational-biology-2020
FAIRsharing presentation at the Japan Science and Technology AgencyPeter McQuilton
A 30 minute seminar presented at the National Bioscience Database Center, part of the Japanese Science and Technology Agency, based in Tokyo, Japan. This presentation covers the FAIR Principles, the aims, methodology and use of FAIRsharing, related projects such as Bioschemas, and international initiatives such as ELIXIR and EOSC.
How are we Faring with FAIR? (and what FAIR is not)Carole Goble
Keynote presented at the workshop FAIRe Data Infrastructures, 15 October 2020
https://www.gmds.de/aktivitaeten/medizinische-informatik/projektgruppenseiten/faire-dateninfrastrukturen-fuer-die-biomedizinische-informatik/workshop-2020/
Remarkably it was only in 2016 that the ‘FAIR Guiding Principles for scientific data management and stewardship’ appeared in Scientific Data. The paper was intended to launch a dialogue within the research and policy communities: to start a journey to wider accessibility and reusability of data and prepare for automation-readiness by supporting findability, accessibility, interoperability and reusability for machines. Many of the authors (including myself) came from biomedical and associated communities. The paper succeeded in its aim, at least at the policy, enterprise and professional data infrastructure level. Whether FAIR has impacted the researcher at the bench or bedside is open to doubt. It certainly inspired a great deal of activity, many projects, a lot of positioning of interests and raised awareness. COVID has injected impetus and urgency to the FAIR cause (good) and also highlighted its politicisation (not so good).
In this talk I’ll make some personal reflections on how we are faring with FAIR: as one of the original principles authors; as a participant in many current FAIR initiatives (particularly in the biomedical sector and for research objects other than data) and as a veteran of FAIR before we had the principles.
FAIR, community standards and data FAIRification: components and recipesSusanna-Assunta Sansone
Overview of FAIR, FAIRsharing and the FAIR Cookbook at the ATI event on Knowledge Graphs: https://github.com/turing-knowledge-graphs/meet-ups/blob/main/symposium-2022.md
Application of recently developed FAIR metrics to the ELIXIR Core Data ResourcesPistoia Alliance
The FAIR (Findable, Accessible, Interoperable and Reusable) principles aim to maximize the discovery and reuse of digital resources. Using recently developed software and metrics to assess FAIRness and supported through an ELIXIR Implementation Study, Michel worked with a subset of ELIXIR Core Data Resources to apply these technologies. In this webinar, he will discuss their approach, findings, and lessons learned towards the understanding and promotion of the FAIR principles.
Turning FAIR into Reality - Role for Libraries dri_ireland
Presentation by Dr. Natalie Harrower, Director Digital Repository of Ireland and European Commission FAIR data expert group member, on what role librarians can play in the FAIR ecosystem. "Applying the FAIR data principles in day-to-day library practice" session by the Research Data Management Working Group, LIBER Steering Committee Research Infrastructures, LIBER2019, Dublin, 26 June 2019
FAIR data: what it means, how we achieve it, and the role of RDASarah Jones
Presentation on FAIR data, the FAIR Data Action Plan developed by the European Commission Expert Group and the role of the Research Data Alliance on implementing FAIR. The presentation was given at the RDAFinland workshop held on 6th June - https://www.csc.fi/web/training/-/rda_and_fair_supporting_finnish_researchers
Being FAIR: FAIR data and model management SSBSS 2017 Summer SchoolCarole Goble
Lecture 1:
Being FAIR: FAIR data and model management
In recent years we have seen a change in expectations for the management of all the outcomes of research – that is the “assets” of data, models, codes, SOPs, workflows. The “FAIR” (Findable, Accessible, Interoperable, Reusable) Guiding Principles for scientific data management and stewardship [1] have proved to be an effective rallying-cry. Funding agencies expect data (and increasingly software) management retention and access plans. Journals are raising their expectations of the availability of data and codes for pre- and post- publication. The multi-component, multi-disciplinary nature of Systems and Synthetic Biology demands the interlinking and exchange of assets and the systematic recording of metadata for their interpretation.
Our FAIRDOM project (http://www.fair-dom.org) supports Systems Biology research projects with their research data, methods and model management, with an emphasis on standards smuggled in by stealth and sensitivity to asset sharing and credit anxiety. The FAIRDOM Platform has been installed by over 30 labs or projects. Our public, centrally hosted Asset Commons, the FAIRDOMHub.org, supports the outcomes of 50+ projects.
Now established as a grassroots association, FAIRDOM has over 8 years of experience of practical asset sharing and data infrastructure at the researcher coal-face ranging across European programmes (SysMO and ERASysAPP ERANets), national initiatives (Germany's de.NBI and Systems Medicine of the Liver; Norway's Digital Life) and European Research Infrastructures (ISBE) as well as in PI's labs and Centres such as the SynBioChem Centre at Manchester.
In this talk I will show explore how FAIRDOM has been designed to support Systems Biology projects and show examples of its configuration and use. I will also explore the technical and social challenges we face.
I will also refer to European efforts to support public archives for the life sciences. ELIXIR (http:// http://www.elixir-europe.org/) the European Research Infrastructure of 21 national nodes and a hub funded by national agreements to coordinate and sustain key data repositories and archives for the Life Science community, improve access to them and related tools, support training and create a platform for dataset interoperability. As the Head of the ELIXIR-UK Node and co-lead of the ELIXIR Interoperability Platform I will show how this work relates to your projects.
[1] Wilkinson et al, The FAIR Guiding Principles for scientific data management and stewardship Scientific Data 3, doi:10.1038/sdata.2016.18
EOSC-Life AGM 2022 Publishing FAIR RI data resources in EOSC.pdfAllyson Lister
FAIRsharing uses collections to create community-specific views of the resource descriptions we store and the relationships among them. This talk describes the work by EOSC-Life Work Package 1 to update and enrich the EOSC-Life collection, which groups together all resources created by EOSC-Life partners. Part of the EOSC-Life AGM 2022 (https://www.eosc-life.eu/news/3rd-agm/).
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
FAIRification is a Team Sport: FAIRsharing and the FAIR Cookbook
1. FAIRification is a Team Sport
Susanna-Assunta Sansone, PhD
AR-BIC 2022 8th Annual Conference, 10-11 March 2022
Slides: https://www.slideshare.net/SusannaSansone
Group: datareadiness.eng.ox.ac.uk
ORCiD: 0000-0001-5306-5690
Twitter: @SusannaASansone
Professor of Data Readiness
Associate Director, Oxford e-Research Centre
ELIXIR
Interoperability Platform ExCo
elixir-europe.org
Founding
Academic Editor
nature.com/sdata
2. Discoveries are made using shared data and this requires data that are:
• Retrievable and structured in standard format(s)
• Self-described so that third parties can make sense of it
The problem
Forbes article on 2016 Data Scientist Report
https://www.forbes.com/sites/gilpress/2016/03/23/data-
preparation-most-time-consuming-least-enjoyable-data-science-
task-survey-says/#276a35e6f637
Data preparation accounts for about 80% of the work of data scientists
3. A set of principles to enhance the
value of all digital resources and its
reuse by humans and machines
Discoverability and reuse of data at scale
4. Globally unique,
resolvable, and
persistent identifiers
To retrieve and
connect data
Community-defined
descriptive metadata
To enhance
discoverability and
interpretability
Community-defined
terminologies
To use the same term
and mean the
same thing
Detailed provenance
and workflows
To contextualize the
data and facilitate use in
applications
Terms of access “as
open as possible, as
closed a necessary”
To understand how data
can be accessed
Terms of use and clear
licenses
To enable innovation
and reuse, ensuring
credit as needed
Findable Accessible Interoperable Reusable
6. FAIR-driven digital transformation by pharmas
• Biopharma R&D productivity can be improved by
implementing the FAIR Principles
• FAIR enables powerful new AI analytics to
access data for machine learning and prediction
• Requirements
▪ financial, technical, training
• Challenges
▪ change the culture, show business value, achieve
the ‘FAIR enough’ on an enterprise scale
8. Making FAIR a reality in the research ecosystem
doi.org/10.2777/1524
9. An intergovernmental organisation that brings
together life science resources
from across Europe, to coordinate them so that
they form a single infrastructure
10. ELIXIR - a sustainable infrastructure for
biological data
11. ELIXIR Nodes:
connecting national data infrastructures
ELIXIR Nodes are the permanent structure, funded
mainly by national roadmap funding, competitive
grants and industry collaborations, and:
• Act as national coordinating entities
• Bring together national experts
• Provide services, databases, tools and resources
12. What services do ELIXIR offer and
in which domains?
Databases and Data Resources
Interoperability Resources
Bioinformatics Tools
Compute Capabilities
Bioinformatics Training Opportunities
Food & Nutrition
+ Toxicology
Domain experts, who are also service providers and/or users,
drive the developments in the Platforms
13. Focus on the interoperability platform
Databases and Data Resources
Interoperability Resources
Bioinformatics Tools
Compute Capabilities
Bioinformatics Training Opportunities
15. IMI2 project guidelines for
open access to publications
and research data
Recommended by
European funders
FAIR service framework: focus on two resources
16.
17. An informative and educational resource, and a service
FAIRsharing provides curated descriptions and relationship graphs of
standards, databases and policies in all disciplines
COMMUNITY STANDARDS
POLICIES
by funders, journals
and other organizations
DATABASES
including repositories
and knowledgebases
Identifiers
Terminologies Guidelines
Formats
19. Guides consumers to discover, select and use these resources with confidence
Helps producers to make their resources more visible, more widely adopted and cited
Total of
over 3595
resources
(March 2022)
repositories
standards
policies
Promoting repositories, standards, policies
20. Search by subject
Powered by our Subject Ontology of 436 terms
https://fairsharing.org/browse/subject
https://github.com/FAIRsharing/subject-ontology
https://www.ebi.ac.uk/ols/ontologies/srao
21. Identifiers
Terminologies Guidelines
Formats
Conceptual model, conceptual
schema, exchange formats
to represent, contain and
move information
Controlled vocabularies,
thesauri, ontologies
to disambiguate terms and
enable semantic
relationships
Minimum information
reporting requirements,
or checklists
to report the same core,
essential information
Unambiguous, persistent and
context-independent schema
to identify data
and metadata elements
Interoperability standards are the pillars of FAIR
Source:
23. Standard organizations, e.g.: Grass-roots groups, e.g.:
Life and biomedical sciences
Identifiers
Terminologies Guidelines
Formats
486
275
151
8
More than 900 data and metadata standards
Source:
24. Standard organizations, e.g.: Grass-roots groups, e.g.:
• Industry-level standards
• Mostly regulators-driven
• Participation is often regulated
• Standards are sold or licenced
• Formal development process, often
less flexible, could be lengthy
• Charges apply to advanced training or
programmatic access
• Mostly research-level standards
• Open to any interested party
• Volunteering efforts
• Standards are free for use
• Development process varies, more
flexible and adaptable to changes
• Minimal or little funds for carry out the
work, let alone provide training
Understanding their life cycle and landscape
Source:
Identifiers
Terminologies Guidelines
Formats
Formulation
Development
Maintenance
26. Translational Medicine
Clinical Developments
URL: beta.fairsharing.org/PistoiaAllianceFIPs
(work in progress!)
A collaboration with their FAIR Implementation WG
Disclaimer: These profiles speak for a limited community and do not represent any company standards
Building and comparing
“FAIR profiles”
27. Clinical Developments
Disclaimer: These profiles speak for a limited community and do not represent any company standards
Snapshot of the semantic and
syntactic standards used
28. Findability
Sitemap.xml, JSON
Markup with Schema.org for
search indexes
DOI unique persistent
identifiers for each record
ORCID for author credit and
authentication
Accessibility
read/write REST API
read OAI-PMH
Interoperability
JSON markup
Standardized semantics
Cross-links to or import from
records in other registries
ROR for organizations (ongoing)
FundRef for funders (ongoing)
Reusability
CC BY 4.0 license
JSON export
The FAIRness of the FAIRsharing
29.
30. Beyond the hype
Large body of generic FAIR
guidance
Motivations
Non-specific guidance for
the life sciences
Ambitions
Target specific situations to deliver a guide with
applied examples
Join academia and industry forces to make the
case for FAIR data management
Build capacity for high quality data
management in the private and public sectors
31. FAIR Cookbook:
turning knowledge into recipes
What is it?
An online, ‘live’ resource
for the life sciences
A collection of recipes
that cover the operation
steps of FAIR data
management
Who is it for?
Who developed it?
Researchers and data
managers professionals
in the life sciences, from
academia and industry
Including ELIXIR
members
faircookbook.elixir-europe.org
32. FAIR Cookbook: learning objectives
Learn how to improve the FAIRness with exemplar datasets
Understand the levels and indicators of FAIRness
Discover open source technologies, tools and services
Find out the required skills
Acknowledge the challenges
faircookbook.elixir-europe.org
33. Recipes that cover all aspects of FAIRness
Recipe summary card, examples:
35. Step by step process
Guidelines, process, description
References
What should I read next?
Ingredients
An idea of tools/skills needed
Examples
Practical
elements, code
snippets
#Python3
#zooma-annotator-script.py
file
def
get_annotations(propertyTy
pe, propertyValues, filters =
""): "””
Get Zooma annotations for
the values of a given
property of a given type.
""”
import requests
annotations = []
no_annotations = []
Where is the value?
36. ● How to measures the FAIRness level of data?
○ For use in the FAIRification processes to define initial/final level of data FAIRness
● How to measures capability and performance of an organization for FAIR data
generation and management?
○ For use at the strategy level to identify investment areas, monitor processes
○ E.g. ability to provide ETL capability, an ontology look-up service, or mapping services
FAIR indicators and capability maturity model
The FAIRification process
37. The capability maturity model
Which capabilities are needed to
improve data reusability?
The optimum level of FAIRness
is a trade-off between desired
data reuse level and cost to
achieve that level
38. The capability maturity model - the ontology example
Which capabilities are needed to
improve data reusability?
The optimum level of FAIRness
is a trade-off between desired
data reuse level and cost to
achieve that level
No use of
ontologies
Use of internal
ontologies
Use of
community
ontologies
+ Ontology service to
manage several
ontologies, mapping,
versioning etc.
+ Term suggestion,
automatic annotation,
terms conflict
resolution etc.
39. No use of
ontologies
Use of internal
ontologies
Use of
community
ontologies
+ Ontology service to
manage several
ontologies, mapping,
versioning etc.
+ Term suggestion,
automatic annotation,
terms conflict
resolution etc.
The capability maturity model - the ontology example
A dedicated recipe
will help to move
from Repeatable
to Defined level
40. +50 life sciences professionals, researchers and data managers
FARIplus
partners
Industry
+
Academia
FAIR Cookbook: creators and contributors
ELIXIR
Nodes
represented
41. A live ever-growing resource:
become part of a community of FAIR experts!
1Identify a chapter and a topic
Findability Accessibility Interoperability Reusability
Infrastructure Applied examples Assessment
2 Choose a way of contributing and see our guidelines
Google Docs
HackMD
Git
Markdown cheat sheet
Get recipe template
Tips and tricks
Submit an
outline
3
You can
discuss it
with the
Editorial
Board
42.
43. Findability
Sitemap.xml, JSON-LD
Markup with Schema.org,
Bioschemas
w3id.org unique persistent
identifiers for each recipe
ORCID for authors
Accessibility
HTTPS protocol
Interoperability
JSON-LD markup
Cross-links to objects in other
registries
incl. Biotools (tools)
FAIRsharing (repositories, standards)
CreDiT attribution ontology
Reusability
CC BY 4.0 license for all
content
The FAIRness of the FAIR Cookbook
44. Watch the webinar for more information,
or watch out for the new one!
Scheduled: 1 June 2022
datascience.nih.gov/nih-data-sharing-and-reuse-
seminar-series
elixir-europe.org/events/fairplus-webinar-
discovering-fair-cookbook
May 2021
faircookbook.elixir-europe.org
45. FAIRification is a team sport,
it takes a village,
but it is no longer optional.
Because better data means
better science!